An automatic pre-processing pipeline for EEG analysis (APP) based on robust statistics

@article{Cruz2018AnAP,
  title={An automatic pre-processing pipeline for EEG analysis (APP) based on robust statistics},
  author={Janir Nuno da Cruz and Vitaly Chicherov and Michael H. Herzog and Patr{\'i}cia Figueiredo},
  journal={Clinical Neurophysiology},
  year={2018},
  volume={129},
  pages={1427-1437}
}
Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI
TLDR
An open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI, which offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters’ preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.
EPOS: EEG Processing Open-Source Scripts
TLDR
A tutorial-like EEG (pre-)processing pipeline to achieve an automated method based on the semi-automated analysis proposed by Delorme and Makeig, and is compared with a selection of existing approaches.
HAPPILEE: The Harvard Automated Processing Pipeline In Low Electrode Electroencephalography, a standardized software for low density EEG and ERP data
TLDR
The HAPPILEE pipeline is proposed as a standardized, automated pipeline optimized for EEG recordings with low density channel layouts of any size and includes post-processing reports of data and pipeline quality metrics to facilitate the evaluation and reporting of data quality and processing-related changes to the data in a standardized manner.
The Maryland analysis of developmental EEG (MADE) pipeline.
TLDR
The Maryland analysis of developmental EEG (MADE) pipeline is developed as an automated preprocessing pipeline compatible with EEG data recorded with different hardware systems, different populations, levels of artifact contamination, and length of recordings.
A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE)
TLDR
The ALICE toolbox aims to build a sustainable algorithm not only to remove artifacts but also to find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge, and implements the novel strategy for consentient labeling of ICs by several experts.
A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography
TLDR
The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG.
DEEP: A dual EEG pipeline for developmental hyperscanning studies
...
...

References

SHOWING 1-10 OF 48 REFERENCES
The PREP pipeline: standardized preprocessing for large-scale EEG analysis
TLDR
It is demonstrated that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results and a multi-stage robust referencing scheme is introduced to deal with the noisy channel-reference interaction.
EEG artifact removal-state-of-the-art and guidelines.
TLDR
This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts, and concludes that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis.
Automated Artifact Removal From the Electroencephalogram
TLDR
This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction.
FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data
TLDR
FieldTrip is an open source software package that is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data.
An Introduction to the Event-Related Potential Technique
TLDR
In An Introduction to the Event-Related Potential Technique, Steve Luck offers the first comprehensive guide to the practicalities of conducting ERP experiments in cognitive neuroscience and related fields, including affective neuroscience and experimental psychopathology.
...
...